


"""simple docstring"""



import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)
def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> int:


	"""simple docstring"""




	print('''Loading config file...'''	)

	def flatten_yaml_as_dict(_lowercase :   Optional[Any]			,   _lowercase :   Union[str, Any]=""			,   _lowercase :   str="."	):
		lowerCAmelCase_      							=			[]
		for k, v in d.items():
			lowerCAmelCase_      							=			parent_key + sep + k if parent_key else k
			if isinstance(_lowercase			,   collections.abc.MutableMapping	):
				items.extend(flatten_yaml_as_dict(_lowercase			,   _lowercase			,   sep=_lowercase	).items()	)
			else:
				items.append((new_key, v)	)
		return dict(_lowercase	)

	lowerCAmelCase_      							=			argparse.Namespace()
	with open(_lowercase			,   '''r'''	) as yaml_file:
		try:
			lowerCAmelCase_      							=			yaml.load(_lowercase			,   Loader=yaml.FullLoader	)

			lowerCAmelCase_      							=			flatten_yaml_as_dict(_lowercase	)
			for k, v in flat_cfg.items():
				setattr(_lowercase			,   _lowercase			,   _lowercase	)
		except yaml.YAMLError as exc:
			logger.error('''Error while loading config file: {}. Error message: {}'''.format(_lowercase			,   str(_lowercase	)	)	)
	return config
def      UpperCAmelCase     (       _lowercase :   List[Any]			,   _lowercase :   str	)						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			MobileViTVaConfig()

	lowerCAmelCase_      							=			False

	# dataset
	if task_name.startswith('''imagenet1k_'''	):
		lowerCAmelCase_      							=			1_0_0_0
		if int(task_name.strip().split('''_'''	)[-1]	) == 3_8_4:
			lowerCAmelCase_      							=			3_8_4
		else:
			lowerCAmelCase_      							=			2_5_6
		lowerCAmelCase_      							=			'''imagenet-1k-id2label.json'''
	elif task_name.startswith('''imagenet21k_to_1k_'''	):
		lowerCAmelCase_      							=			2_1_0_0_0
		if int(task_name.strip().split('''_'''	)[-1]	) == 3_8_4:
			lowerCAmelCase_      							=			3_8_4
		else:
			lowerCAmelCase_      							=			2_5_6
		lowerCAmelCase_      							=			'''imagenet-22k-id2label.json'''
	elif task_name.startswith('''ade20k_'''	):
		lowerCAmelCase_      							=			1_5_1
		lowerCAmelCase_      							=			5_1_2
		lowerCAmelCase_      							=			'''ade20k-id2label.json'''
		lowerCAmelCase_      							=			True
	elif task_name.startswith('''voc_'''	):
		lowerCAmelCase_      							=			2_1
		lowerCAmelCase_      							=			5_1_2
		lowerCAmelCase_      							=			'''pascal-voc-id2label.json'''
		lowerCAmelCase_      							=			True

	# orig_config
	lowerCAmelCase_      							=			load_orig_config_file(_lowercase	)
	assert getattr(_lowercase			,   '''model.classification.name'''			,   -1	) == "mobilevit_v2", "Invalid model"
	lowerCAmelCase_      							=			getattr(_lowercase			,   '''model.classification.mitv2.width_multiplier'''			,   1.0	)
	assert (
	    getattr(_lowercase			,   '''model.classification.mitv2.attn_norm_layer'''			,   -1	) == "layer_norm_2d"
	), "Norm layers other than layer_norm_2d is not supported"
	lowerCAmelCase_      							=			getattr(_lowercase			,   '''model.classification.activation.name'''			,   '''swish'''	)
	# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

	if is_segmentation_model:
		lowerCAmelCase_      							=			getattr(_lowercase			,   '''model.segmentation.output_stride'''			,   1_6	)
		if "_deeplabv3" in task_name:
			lowerCAmelCase_      							=			getattr(_lowercase			,   '''model.segmentation.deeplabv3.aspp_rates'''			,   [1_2, 2_4, 3_6]	)
			lowerCAmelCase_      							=			getattr(_lowercase			,   '''model.segmentation.deeplabv3.aspp_out_channels'''			,   5_1_2	)
			lowerCAmelCase_      							=			getattr(_lowercase			,   '''model.segmentation.deeplabv3.aspp_dropout'''			,   0.1	)

    # id2label
	lowerCAmelCase_      							=			'''huggingface/label-files'''
	lowerCAmelCase_      							=			json.load(open(hf_hub_download(_lowercase			,   _lowercase			,   repo_type='''dataset'''	)			,   '''r'''	)	)
	lowerCAmelCase_      							=			{int(_lowercase	): v for k, v in idalabel.items()}
	lowerCAmelCase_      							=			idalabel
	lowerCAmelCase_      							=			{v: k for k, v in idalabel.items()}

	return config
def      UpperCAmelCase     (       _lowercase :   List[str]			,   _lowercase :   Union[str, Any]			,   _lowercase :   Optional[Any]	)						-> List[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			dct.pop(_lowercase	)
	lowerCAmelCase_      							=			val
def      UpperCAmelCase     (       _lowercase :   Tuple			,   _lowercase :   int=False	)						-> Dict:


	"""simple docstring"""




	if base_model:
		lowerCAmelCase_      							=			''''''
	else:
		lowerCAmelCase_      							=			'''mobilevitv2.'''

	lowerCAmelCase_      							=			[]
	for k in state_dict.keys():
		if k[:8] == "encoder.":
			lowerCAmelCase_      							=			k[8:]
		else:
			lowerCAmelCase_      							=			k

		if ".block." in k:
			lowerCAmelCase_      							=			k_new.replace('''.block.'''			,   '''.'''	)
		if ".conv." in k:
			lowerCAmelCase_      							=			k_new.replace('''.conv.'''			,   '''.convolution.'''	)
		if ".norm." in k:
			lowerCAmelCase_      							=			k_new.replace('''.norm.'''			,   '''.normalization.'''	)

		if "conv_1." in k:
			lowerCAmelCase_      							=			k_new.replace('''conv_1.'''			,   F"""{model_prefix}conv_stem."""	)
		for i in [1, 2]:
			if F"""layer_{i}.""" in k:
				lowerCAmelCase_      							=			k_new.replace(F"""layer_{i}."""			,   F"""{model_prefix}encoder.layer.{i-1}.layer."""	)
		if ".exp_1x1." in k:
			lowerCAmelCase_      							=			k_new.replace('''.exp_1x1.'''			,   '''.expand_1x1.'''	)
		if ".red_1x1." in k:
			lowerCAmelCase_      							=			k_new.replace('''.red_1x1.'''			,   '''.reduce_1x1.'''	)

		for i in [3, 4, 5]:
			if F"""layer_{i}.0.""" in k:
				lowerCAmelCase_      							=			k_new.replace(F"""layer_{i}.0."""			,   F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""	)
			if F"""layer_{i}.1.local_rep.0.""" in k:
				lowerCAmelCase_      							=			k_new.replace(F"""layer_{i}.1.local_rep.0."""			,   F"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""	)
			if F"""layer_{i}.1.local_rep.1.""" in k:
				lowerCAmelCase_      							=			k_new.replace(F"""layer_{i}.1.local_rep.1."""			,   F"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""	)

		for i in [3, 4, 5]:
			if i == 3:
				lowerCAmelCase_      							=			[0, 1]
			elif i == 4:
				lowerCAmelCase_      							=			[0, 1, 2, 3]
			elif i == 5:
				lowerCAmelCase_      							=			[0, 1, 2]

			for j in j_in:
				if F"""layer_{i}.1.global_rep.{j}.""" in k:
					lowerCAmelCase_      							=			k_new.replace(
					    F"""layer_{i}.1.global_rep.{j}."""			,   F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""	)
			if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
				lowerCAmelCase_      							=			k_new.replace(
				    F"""layer_{i}.1.global_rep.{j+1}."""			,   F"""{model_prefix}encoder.layer.{i-1}.layernorm."""	)

			if F"""layer_{i}.1.conv_proj.""" in k:
				lowerCAmelCase_      							=			k_new.replace(F"""layer_{i}.1.conv_proj."""			,   F"""{model_prefix}encoder.layer.{i-1}.conv_projection."""	)

		if "pre_norm_attn.0." in k:
			lowerCAmelCase_      							=			k_new.replace('''pre_norm_attn.0.'''			,   '''layernorm_before.'''	)
		if "pre_norm_attn.1." in k:
			lowerCAmelCase_      							=			k_new.replace('''pre_norm_attn.1.'''			,   '''attention.'''	)
		if "pre_norm_ffn.0." in k:
			lowerCAmelCase_      							=			k_new.replace('''pre_norm_ffn.0.'''			,   '''layernorm_after.'''	)
		if "pre_norm_ffn.1." in k:
			lowerCAmelCase_      							=			k_new.replace('''pre_norm_ffn.1.'''			,   '''ffn.conv1.'''	)
		if "pre_norm_ffn.3." in k:
			lowerCAmelCase_      							=			k_new.replace('''pre_norm_ffn.3.'''			,   '''ffn.conv2.'''	)

		if "classifier.1." in k:
			lowerCAmelCase_      							=			k_new.replace('''classifier.1.'''			,   '''classifier.'''	)

		if "seg_head." in k:
			lowerCAmelCase_      							=			k_new.replace('''seg_head.'''			,   '''segmentation_head.'''	)
		if ".aspp_layer." in k:
			lowerCAmelCase_      							=			k_new.replace('''.aspp_layer.'''			,   '''.'''	)
		if ".aspp_pool." in k:
			lowerCAmelCase_      							=			k_new.replace('''.aspp_pool.'''			,   '''.'''	)

		rename_keys.append((k, k_new)	)
	return rename_keys
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			[]
	for k in state_dict.keys():
		if k.startswith('''seg_head.aux_head.'''	):
			keys_to_ignore.append(_lowercase	)
	for k in keys_to_ignore:
		state_dict.pop(_lowercase			,   _lowercase	)
def      UpperCAmelCase     (       )						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
	lowerCAmelCase_      							=			Image.open(requests.get(_lowercase			,   stream=_lowercase	).raw	)
	return im
@torch.no_grad()
def      UpperCAmelCase     (       _lowercase :   List[str]			,   _lowercase :   Optional[int]			,   _lowercase :   str			,   _lowercase :   Any	)						-> str:


	"""simple docstring"""




	lowerCAmelCase_      							=			get_mobilevitva_config(_lowercase			,   _lowercase	)

	# load original state_dict
	lowerCAmelCase_      							=			torch.load(_lowercase			,   map_location='''cpu'''	)

	# load huggingface model
	if task_name.startswith('''ade20k_'''	) or task_name.startswith('''voc_'''	):
		lowerCAmelCase_      							=			MobileViTVaForSemanticSegmentation(_lowercase	).eval()
		lowerCAmelCase_      							=			False
	else:
		lowerCAmelCase_      							=			MobileViTVaForImageClassification(_lowercase	).eval()
		lowerCAmelCase_      							=			False

	# remove and rename some keys of load the original model
	lowerCAmelCase_      							=			checkpoint
	remove_unused_keys(_lowercase	)
	lowerCAmelCase_      							=			create_rename_keys(_lowercase			,   base_model=_lowercase	)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(_lowercase			,   _lowercase			,   _lowercase	)

	# load modified state_dict
	model.load_state_dict(_lowercase	)

	# Check outputs on an image, prepared by MobileViTImageProcessor
	lowerCAmelCase_      							=			MobileViTImageProcessor(crop_size=config.image_size			,   size=config.image_size + 3_2	)
	lowerCAmelCase_      							=			image_processor(images=prepare_img()			,   return_tensors='''pt'''	)
	lowerCAmelCase_      							=			model(**_lowercase	)

	# verify classification model
	if task_name.startswith('''imagenet'''	):
		lowerCAmelCase_      							=			outputs.logits
		lowerCAmelCase_      							=			logits.argmax(-1	).item()
		print('''Predicted class:'''			,   model.config.idalabel[predicted_class_idx]	)
		if task_name.startswith('''imagenet1k_256'''	) and config.width_multiplier == 1.0:
			# expected_logits for base variant
			lowerCAmelCase_      							=			torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]	)
			assert torch.allclose(logits[0, :3]			,   _lowercase			,   atol=1E-4	)

	Path(_lowercase	).mkdir(exist_ok=_lowercase	)
	print(F"""Saving model {task_name} to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)
	print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
	image_processor.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--task',
		    default='imagenet1k_256',
		    type=str,
		    help=(
		        'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
		        '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
		    ),
		    choices=[
		        'imagenet1k_256',
		        'imagenet1k_384',
		        'imagenet21k_to_1k_256',
		        'imagenet21k_to_1k_384',
		        'ade20k_deeplabv3',
		        'voc_deeplabv3',
		    ],
		)

		parser.add_argument(
		    '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
		)
		parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
		)

		lowercase_							=							parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
		)



"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{'vocab_file': 'vocab.txt'}

lowercase_							=							{
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

lowercase_							=							{
    'YituTech/conv-bert-base': 5_12,
    'YituTech/conv-bert-medium-small': 5_12,
    'YituTech/conv-bert-small': 5_12,
}


lowercase_							=							{
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[int]			       =VOCAB_FILES_NAMES
				lowerCamelCase     :       List[str]			       =PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase     :       str			       =PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase     :       Any			       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase     :       List[str]			       =ConvBertTokenizer







				def __init__( self      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=True      ,	UpperCAmelCase="[UNK]"      ,	UpperCAmelCase="[SEP]"      ,	UpperCAmelCase="[PAD]"      ,	UpperCAmelCase="[CLS]"      ,	UpperCAmelCase="[MASK]"      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(
					    UpperCAmelCase      ,	tokenizer_file=UpperCAmelCase      ,	do_lower_case=UpperCAmelCase      ,	unk_token=UpperCAmelCase      ,	sep_token=UpperCAmelCase      ,	pad_token=UpperCAmelCase      ,	cls_token=UpperCAmelCase      ,	mask_token=UpperCAmelCase      ,	tokenize_chinese_chars=UpperCAmelCase      ,	strip_accents=UpperCAmelCase      ,	**UpperCAmelCase      ,	)

					lowerCAmelCase_      							=			json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
					if (
					    normalizer_state.get('''lowercase'''      ,	UpperCAmelCase   ) != do_lower_case
					    or normalizer_state.get('''strip_accents'''      ,	UpperCAmelCase   ) != strip_accents
					    or normalizer_state.get('''handle_chinese_chars'''      ,	UpperCAmelCase   ) != tokenize_chinese_chars
					):
						lowerCAmelCase_      							=			getattr(UpperCAmelCase      ,	normalizer_state.pop('''type'''   )   )
						lowerCAmelCase_      							=			do_lower_case
						lowerCAmelCase_      							=			strip_accents
						lowerCAmelCase_      							=			tokenize_chinese_chars
						lowerCAmelCase_      							=			normalizer_class(**UpperCAmelCase   )

					lowerCAmelCase_      							=			do_lower_case







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase=None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
						output += token_ids_a + [self.sep_token_id]

					return output







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			[self.sep_token_id]
					lowerCAmelCase_      							=			[self.cls_token_id]
					if token_ids_a is None:
						return len(cls + token_ids_a + sep   ) * [0]
					return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase = None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self._tokenizer.model.save(UpperCAmelCase      ,	name=UpperCAmelCase   )
					return tuple(UpperCAmelCase   )